Using recycled aggregate s from construction and demolition waste can preserve natural aggregate resources, reduce demand of landfill, and contribute to sustainable built environment. This study provides a comprehensive review on recycled aggregate (RA) and recycled aggregate concrete (RAC) regarding their history, recycling, reuse and manufacture process, inherent defects (e.g. existing of additional interfacial transition zones in RAC), and materials properties. Specifically, these properties of RAC include fresh concrete workability, physical and chemical properties (i.e. density, carbonation depth, and chloride ion penetration), mechanical properties (i.e. compressive, flexural, and splitting tensile strength as well as elastic modulus), and long-term performance (i.e. freezing-thawing resistance, alkali-silica reaction resistance, creep, and dry shrinkage). On top of that, methods for improving RAC mechanical properties and long-term performance are summarized and categorized into three groups, i.e. (1) reduction of recycled aggregate porosity, (2) reduction of old mortar layer on recycled aggregate surface, and (3) property improvement without recycled aggregate modification (i.e. different concrete mixing design and addition of fibre reinforcement). Next, current regression-based models and artificial intelligence models on the prediction of compressive strength, modulus, and compressive stress-strain curves of RAC are reviewed and the ir limitations of those models are discussed. Furthermore, the state-of-the-art RAC applications are presented. Additionally, challenges of RAC application are reviewed taking China as an example. The link between material from CDW and EU green policy are discussed by analysing the previous research projects funded by European Commission. Finally, future perspectives of RAC research focus are discussed, i.e. development of “green” treatment methods on recycled aggregate s , further direction on nanoparticle application in RAC, and the establishment of database for RAC strength prediction.

A Comprehensive Review on Recycled Aggregate and Recycled Aggregate Concrete

Despite the extensive use of mortars materials in constructions over the last decades, there is not yet a reliable and robust method, available in the literature, which can estimate its strength based on its mix parameters. This limitation is due to the highly nonlinear relation between the mortar’s compressive strength and the mixed components. In this paper, the application of artificial intelligence techniques toward the prediction of the compressive strength of cement-based mortar materials with or without metakaolin has been investigated. Specifically, surrogate models (such as artificial neural network, ANN and adaptive neuro-fuzzy inference system, ANFIS models) have been developed to the prediction of the compressive strength of mortars trained using experimental data available in the literature. The comparison of the derived results with the experimental findings demonstrates the ability of both ANN and ANFIS models to approximate the compressive strength of mortars in a reliable and robust manner. Although ANFIS was able to obtain higher performance prediction to estimate the compressive strength of mortars compared to ANN model, it was found through the verification process of some other additional data, the ANFIS model has overfitted the data. Therefore, the developed ANN model has been introduced as the best predictive technique for solving problem of the compressive strength of mortars. Furthermore, using the optimum developed model an ambitious attempt to reveal the nature of mortar materials has been made.

A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength

Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models

Machine learning-based prediction for compressive and flexural strengths of steel fiber-reinforced concrete

Machine learning for structural engineering: A state-of-the-art review

Recycled aggregate concrete is used as an alternative material in construction engineering, aiming to environmental protection and sustainable development. However, the compressive strength of this concrete material is considered as a crucial parameter and an important concern for construction engineers regarding its application. In the present work, the 28-days compressive strength of recycled aggregate concrete is investigated through four artificial intelligence techniques based on a meta-heuristic search of sociopolitical algorithm (i.e. ICA) and XGBoost, called the ICA-XGBoost model. Based on performance indices, the optimum among these developed models proved to be ICA-XGBoost model. Namely, findings demonstrated that the proposed ICA-XGBoost model performed better than the other models (i.e. ICA-ANN, ICA-SVR, and ICA-ANFIS models) in estimating compressive strength of recycled aggregate concrete. The suggested model can be used in construction engineering in order to ensure adequate mechanical performance of the recycled aggregate concrete and allow its safe use for building purposes.

A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model

Aiming at the problems of small target scale, vulnerable to background interference and insufficient feature utilization in remote sensing image target detection task, a single stage target detection algorithm based on feature enhancement and feature fusion is proposed. Based on CenterNet, a feature enhancement module is designed, which enriches and strengthens the features of small targets and solves the problem of low accuracy caused by small targets and background interference. Then BiFPN mini multi-scale feature fusion structure is used to strengthen the feature expression ability of the target and solve the problem of insufficient feature utilization. This algorithm is implemented in the average detection accuracy on UCAS_AOD dataset reaches 84.9%. The experimental results show that the improved measures in this paper effectively improve the target detection accuracy for remote sensing images.

Jin Duan

Papers

A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model

Remote sensing image target detection algorithm based on CenterNet